Crowd simulation has been an important research field due to its
diverse range of applications that include film production, military
simulation, and urban planning. A challenging problem is to
provide simple yet effective control over captured and simulated
crowds to synthesize intended group motions. We present a new
method that blends existing crowd data to generate a new crowd
animation. The new animation can include an arbitrary number
of agents, extends for an arbitrary duration, and yields a naturallooking
mixture of the input crowd data. The main benefit of this
approach is to create new spatio-temporal crowd behavior in an intuitive
and predictable manner. It is accomplished by introducing
a morphable crowd model that allows us to encode the formations
and individual trajectories in crowd data. Then, its original spatiotemporal
behavior can be reconstructed and interpolated at an arbitrary
scale using our morphable model.